
Cybersickness prediction - Although deep learning classifiers are the most suitable approach for CS prediction, deep neural networks are black boxes that are very difficult to grasp. On the other hand, symbolic machine learning algorithms enable a straight understanding of decision paths [1]}. The human-readable characteristic of symbolic machine learning models such as decision tree and random forest can help understand the discomfort manifestation reasons [2]}. Additionally, a recent approach applies strategies to make deep learning models explainable [3]}, although the literature is still scarce on the topic. Explainable AI can be an excellent way to better the two worlds: the human understanding of symbolic machine learning with the accurate prediction of deep neural networks.

Data labeling - Another critical problem in CS research is associated with data labeling. Researchers generally collect verbal [1]}, [5]}, haptic, or brain signal feedback to construct the ground truth of sickness. While verbal feedback is highly subjective and different for each participant, collecting haptic feedback when participants are in discomfort can often be corrupted by the delay associated with participant feedback.

Data from underrepresented groups - Another not resolved challenge is acquiring data considering underrepresented user-profiles [6]} such as specific impairments or health issues [7]}.

Tasks in VR - A recent work [8]} pointed out that specific tasks can produce different results of CS for different user profiles and groups. Additionally, in a recent work [9]} we suggested the importance of a better understanding of the correlation between profile attributes (such as gender, age, XR experience, and other individual characteristics) and gameplay elements, and also how outcomes obtained from profile attributes can be used to tag XR experiences according to distinct groups of users. In this context, studies involving machine learning models combined with an evaluation of individual tasks associated with CS causes in XR games may produce a more profound study isolating any other XR possible influences on CS results.

